
import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	(EulerDiscreteScheduler,)
	_A								=	1_0







	def   _lowerCamelCase							(   self						:Dict	,  **a						:Optional[int]		)						->					Optional[int]:
								__UpperCamelCase      :  Tuple       =   {
								    "num_train_timesteps": 1_1_0_0,
								    "beta_start": 0.0001,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**a		)
								return config







	def   _lowerCamelCase							(   self						:str		)						->					Dict:
								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=a		)







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					List[Any]:
								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]	,  [0.0002, 0.002, 0.02]		):
															self.check_over_configs(beta_start=a	,  beta_end=a		)







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					List[str]:
								for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=a		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					List[str]:
								for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=a		)







	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					Tuple:
								__UpperCamelCase      :  Any       =   self.scheduler_classes[0]
								__UpperCamelCase      :  int       =   self.get_scheduler_config()
								__UpperCamelCase      :  int       =   scheduler_class(**a		)

								scheduler.set_timesteps(self.num_inference_steps		)

								__UpperCamelCase      :  Tuple       =   torch.manual_seed(0		)

								__UpperCamelCase      :  str       =   self.dummy_model()
								__UpperCamelCase      :  Optional[int]       =   self.dummy_sample_deter * scheduler.init_noise_sigma
								__UpperCamelCase      :  Dict       =   sample.to(a		)

								for i, t in enumerate(scheduler.timesteps		):
															__UpperCamelCase      :  Tuple       =   scheduler.scale_model_input(a	,  a		)

															__UpperCamelCase      :  Any       =   model(a	,  a		)

															__UpperCamelCase      :  List[Any]       =   scheduler.step(a	,  a	,  a	,  generator=a		)
															__UpperCamelCase      :  Dict       =   output.prev_sample

								__UpperCamelCase      :  List[str]       =   torch.sum(torch.abs(a		)		)
								__UpperCamelCase      :  Any       =   torch.mean(torch.abs(a		)		)

								assert abs(result_sum.item() - 10.0807		) < 1E-2
								assert abs(result_mean.item() - 0.0131		) < 1E-3







	def   _lowerCamelCase							(   self						:Dict		)						->					Dict:
								__UpperCamelCase      :  int       =   self.scheduler_classes[0]
								__UpperCamelCase      :  Tuple       =   self.get_scheduler_config(prediction_type="v_prediction"		)
								__UpperCamelCase      :  Dict       =   scheduler_class(**a		)

								scheduler.set_timesteps(self.num_inference_steps		)

								__UpperCamelCase      :  List[Any]       =   torch.manual_seed(0		)

								__UpperCamelCase      :  Dict       =   self.dummy_model()
								__UpperCamelCase      :  List[Any]       =   self.dummy_sample_deter * scheduler.init_noise_sigma
								__UpperCamelCase      :  Any       =   sample.to(a		)

								for i, t in enumerate(scheduler.timesteps		):
															__UpperCamelCase      :  str       =   scheduler.scale_model_input(a	,  a		)

															__UpperCamelCase      :  Optional[Any]       =   model(a	,  a		)

															__UpperCamelCase      :  Any       =   scheduler.step(a	,  a	,  a	,  generator=a		)
															__UpperCamelCase      :  Union[str, Any]       =   output.prev_sample

								__UpperCamelCase      :  List[str]       =   torch.sum(torch.abs(a		)		)
								__UpperCamelCase      :  str       =   torch.mean(torch.abs(a		)		)

								assert abs(result_sum.item() - 0.0002		) < 1E-2
								assert abs(result_mean.item() - 2.2_6_7_6E-0_6		) < 1E-3







	def   _lowerCamelCase							(   self						:Tuple		)						->					str:
								__UpperCamelCase      :  List[str]       =   self.scheduler_classes[0]
								__UpperCamelCase      :  int       =   self.get_scheduler_config()
								__UpperCamelCase      :  List[Any]       =   scheduler_class(**a		)

								scheduler.set_timesteps(self.num_inference_steps	,  device=a		)

								__UpperCamelCase      :  List[str]       =   torch.manual_seed(0		)

								__UpperCamelCase      :  Dict       =   self.dummy_model()
								__UpperCamelCase      :  str       =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								__UpperCamelCase      :  str       =   sample.to(a		)

								for t in scheduler.timesteps:
															__UpperCamelCase      :  Optional[int]       =   scheduler.scale_model_input(a	,  a		)

															__UpperCamelCase      :  str       =   model(a	,  a		)

															__UpperCamelCase      :  List[Any]       =   scheduler.step(a	,  a	,  a	,  generator=a		)
															__UpperCamelCase      :  Dict       =   output.prev_sample

								__UpperCamelCase      :  Any       =   torch.sum(torch.abs(a		)		)
								__UpperCamelCase      :  Tuple       =   torch.mean(torch.abs(a		)		)

								assert abs(result_sum.item() - 10.0807		) < 1E-2
								assert abs(result_mean.item() - 0.0131		) < 1E-3







	def   _lowerCamelCase							(   self						:Dict		)						->					List[Any]:
								__UpperCamelCase      :  Optional[int]       =   self.scheduler_classes[0]
								__UpperCamelCase      :  str       =   self.get_scheduler_config()
								__UpperCamelCase      :  Any       =   scheduler_class(**a	,  use_karras_sigmas=a		)

								scheduler.set_timesteps(self.num_inference_steps	,  device=a		)

								__UpperCamelCase      :  int       =   torch.manual_seed(0		)

								__UpperCamelCase      :  Tuple       =   self.dummy_model()
								__UpperCamelCase      :  Any       =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								__UpperCamelCase      :  Optional[Any]       =   sample.to(a		)

								for t in scheduler.timesteps:
															__UpperCamelCase      :  str       =   scheduler.scale_model_input(a	,  a		)

															__UpperCamelCase      :  int       =   model(a	,  a		)

															__UpperCamelCase      :  int       =   scheduler.step(a	,  a	,  a	,  generator=a		)
															__UpperCamelCase      :  Any       =   output.prev_sample

								__UpperCamelCase      :  Any       =   torch.sum(torch.abs(a		)		)
								__UpperCamelCase      :  Dict       =   torch.mean(torch.abs(a		)		)

								assert abs(result_sum.item() - 124.52299499511719		) < 1E-2
								assert abs(result_mean.item() - 0.16213932633399963		) < 1E-3

# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowercase    :   List[Any]						=			'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]=None)   ->       List[str]:






							'''simple docstring'''





							if subparsers is not None:
														__UpperCamelCase      :  int       =   subparsers.add_parser("tpu-config"		,    description=_description)
							else:
														__UpperCamelCase      :  Dict       =   argparse.ArgumentParser("Accelerate tpu-config command"		,    description=_description)
							# Core arguments
							__UpperCamelCase      :  Optional[Any]       =   parser.add_argument_group(
							    "Config Arguments"		,    "Arguments that can be configured through `accelerate config`.")
							config_args.add_argument(
							    "--config_file"		,    type=_lowerCamelCase		,    default=_lowerCamelCase		,    help="Path to the config file to use for accelerate."		,    )
							config_args.add_argument(
							    "--tpu_name"		,    default=_lowerCamelCase		,    help="The name of the TPU to use. If not specified, will use the TPU specified in the config file."		,    )
							config_args.add_argument(
							    "--tpu_zone"		,    default=_lowerCamelCase		,    help="The zone of the TPU to use. If not specified, will use the zone specified in the config file."		,    )
							__UpperCamelCase      :  Tuple       =   parser.add_argument_group("TPU Arguments"		,    "Arguments for options ran inside the TPU.")
							pod_args.add_argument(
							    "--use_alpha"		,    action="store_true"		,    help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`."		,    )
							pod_args.add_argument(
							    "--command_file"		,    default=_lowerCamelCase		,    help="The path to the file containing the commands to run on the pod on startup."		,    )
							pod_args.add_argument(
							    "--command"		,    action="append"		,    nargs="+"		,    help="A command to run on the pod. Can be passed multiple times."		,    )
							pod_args.add_argument(
							    "--install_accelerate"		,    action="store_true"		,    help="Whether to install accelerate on the pod. Defaults to False."		,    )
							pod_args.add_argument(
							    "--accelerate_version"		,    default="latest"		,    help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub."		,    )
							pod_args.add_argument(
							    "--debug"		,    action="store_true"		,    help="If set, will print the command that would be run instead of running it.")

							if subparsers is not None:
														parser.set_defaults(func=_lowerCamelCase)
							return parser




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     List[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   None

							# Get the default from the config file if it exists.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase):
														__UpperCamelCase      :  Dict       =   load_config_from_file(args.config_file)
														if not args.command_file and defaults.command_file is not None and not args.command:
																					__UpperCamelCase      :  List[Any]       =   defaults.command_file
														if not args.command and defaults.commands is not None:
																					__UpperCamelCase      :  int       =   defaults.commands
														if not args.tpu_name:
																					__UpperCamelCase      :  int       =   defaults.tpu_name
														if not args.tpu_zone:
																					__UpperCamelCase      :  Optional[Any]       =   defaults.tpu_zone
							if args.accelerate_version == "dev":
														__UpperCamelCase      :  List[str]       =   "git+https://github.com/huggingface/accelerate.git"
							elif args.accelerate_version == "latest":
														__UpperCamelCase      :  Optional[int]       =   "accelerate -U"
							elif isinstance(parse(args.accelerate_version)		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   F'accelerate=={args.accelerate_version}'

							if not args.command_file and not args.command:
														raise ValueError("You must specify either a command file or a command to run on the pod.")

							if args.command_file:
														with open(args.command_file		,    "r") as f:
																					__UpperCamelCase      :  List[Any]       =   [f.read().splitlines()]

    # To turn list of lists into list of strings
							if isinstance(args.command[0]		,    _lowerCamelCase):
														__UpperCamelCase      :  Union[str, Any]       =   [line for cmd in args.command for line in cmd]
							# Default to the shared folder and install accelerate
							__UpperCamelCase      :  str       =   ["cd /usr/share"]
							if args.install_accelerate:
														new_cmd += [F'pip install {args.accelerate_version}']
							new_cmd += args.command
							__UpperCamelCase      :  str       =   "; ".join(_lowerCamelCase)

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							__UpperCamelCase      :  List[Any]       =   ["gcloud"]
							if args.use_alpha:
														cmd += ["alpha"]
							cmd += [
							    "compute",
							    "tpus",
							    "tpu-vm",
							    "ssh",
							    args.tpu_name,
							    "--zone",
							    args.tpu_zone,
							    "--command",
							    args.command,
							    "--worker",
							    "all",
							]
							if args.debug:
														print(F'Running {" ".join(_lowerCamelCase)}')
														return
							subprocess.run(_lowerCamelCase)
							print("Successfully setup pod.")




def       _SCREAMING_SNAKE_CASE   (						)   ->       List[Any]:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[Any]       =   tpu_command_parser()
							__UpperCamelCase      :  Optional[Any]       =   parser.parse_args()

							tpu_command_launcher(_lowerCamelCase)